Metrics

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. object CatCountFreq extends DiscreteMetric

   

5. object CountDiscrete extends DiscreteMetric

   

6. object CountDistinct extends DiscreteMetric

   

7. object CountMissValues extends ContinuousMetric

   

8. object CountMissValuesDiscrete extends DiscreteMetric

   

9. object Kurtosis extends ContinuousMetric

   

10. object Max extends ContinuousMetric

    

11. object Mean extends ContinuousMetric

    

12. object Median extends ContinuousMetric

    

13. object Min extends ContinuousMetric

    

14. object Percentile25 extends ContinuousMetric

    

15. object Percentile75 extends ContinuousMetric

    

16. object Skewness extends ContinuousMetric

    

17. object Stddev extends ContinuousMetric

    

18. object Sum extends ContinuousMetric

    

19. object Variance extends ContinuousMetric

    

20. final def asInstanceOf[T0]: T0

    

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21. def categoryCountFreqDataframe(e: Column, dataInit: DataFrame): (Column, DataFrame)

    

    Function to compute the Dataframe with Category, Count and Frequencies obtain from the initial Dataframe

    Function to compute the Dataframe with Category, Count and Frequencies obtain from the initial Dataframe

    e

    : column of the variable.

    dataInit

    : initial DataFrame.

    returns

    (Column, DataFrame) : tuple2 of the column of the variable and the initial Dataframe

22. def clone(): AnyRef

    

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    protected[java.lang]

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23. def computeContinuousMetric(dataset: DataFrame, continuousAttributes: List[String], operations: List[ContinuousMetric]): Option[DataFrame]

    

    Function to compute the DataFrame metrics by row

    Function to compute the DataFrame metrics by row

    dataset

    : initial DataFrame.

    continuousAttributes

    : name list of all variables.

    operations

    : list of metrics you want to calculate.

    returns

    DataFrame : DataFrame metric of all variables by row.

24. def computeDiscretMetric(dataInit: DataFrame, discreteAttrs: List[String], operations: List[DiscreteMetric]): Option[DataFrame]

    

    Function to compute and to combine all the partial DataFrame metric by variable (to get one DataFrame by row).

    Function to compute and to combine all the partial DataFrame metric by variable (to get one DataFrame by row).

    dataInit

    : initial DataFrame.

    discreteAttrs

    : name of the variable.

    operations

    : list of metrics you want to calculate.

    returns

    DataFrame : DataFrame with alle the metric by variable by row

25. val continuousMetrics: List[ContinuousMetric]

    

    List of all available metrics

26. def customCatCountFreq(colNameDataCatCount: (Column, DataFrame)): Column

    

    Customize catCountFreq for discrete variable

27. def customCategory(colNameDataCatCount: (Column, DataFrame)): Column

    

    Customize Category for discrete variable

    Customize Category for discrete variable

    colNameDataCatCount

    : couple of name of the variable and the dataframe obtain from categoryCountFreqDataframe()

    returns

    Column : the computed value of the function metricCategory

28. def customCountDiscrete(colNameDataCatCount: (Column, DataFrame)): Column

    

    Customize Count Discrete for discrete variable

    Customize Count Discrete for discrete variable

    colNameDataCatCount

    : couple of name of the variable and the dataframe obtain from categoryCountFreqDataframe()

    returns

    Column : the computed value of the function metricCountDiscret

29. def customCountDistinct(colNameDataCatCount: (Column, DataFrame)): Column

    

    Customize CountDistinct for discrete variable

    Customize CountDistinct for discrete variable

    colNameDataCatCount

    : couple of name of the variable and the dataframe obtain from categoryCountFreqDataframe()

    returns

    Column : the computed value of the function metricCountDistinct

30. def customCountMissValues(e: Column): Column

    

    Customize missing values

    Customize missing values

    e

    : the column

    returns

    Integer : the number of missing values, NaN values and null values

31. def customCountMissValuesDiscrete(colNameDataCatCount: (Column, DataFrame)): Column

    

    Customize number of Missing Values for discrete variable

    Customize number of Missing Values for discrete variable

    colNameDataCatCount

    : couple of name of the variable and the dataframe obtain from categoryCountFreqDataframe()

    returns

    Column : the computed value of the function metricMissingValues

32. def customFrequencies(colNameDataCatCount: (Column, DataFrame)): Column

    

    Customize Count Distinct for discrete variable

    Customize Count Distinct for discrete variable

    colNameDataCatCount

    : couple of name of the variable and the dataframe obtain from categoryCountFreqDataframe()

    returns

    Column : the computed value of the function metricCountDistinct

33. def customMean(e: Column): Column

    

    Customize mean of the column e

    Customize mean of the column e

    e

    : the column

    returns

    Integer : the computed value of the mean

34. def customMedian(e: Column): Column

    

    Customize Median of the column e

    Customize Median of the column e

    e

    : the column

    returns

    Integer : the computed value of the Median

35. def customMetric(e: Column, metricName: String, metricFunction: (Column) ⇒ Column): Column

    

    Customize function metric in the case continuous variabes used for : mean, variance and stddev

    Customize function metric in the case continuous variabes used for : mean, variance and stddev

    e

    : the column

    metricName

    : the name of the metric

    metricFunction

    : the metric function

    returns

    : the computed value of the function

36. def customMetricDiscret(e: Column, dataCategoryCount: DataFrame, metricName: String, metricFunction: (DataFrame) ⇒ Column): Column

    

    Customize Metric Discret for discrete variable

    Customize Metric Discret for discrete variable

    e

    : name of the column

    dataCategoryCount

    : the dataframe obtain from categoryCountFreqDataframe()

    metricName

    : te metric name

    metricFunction

    : the metric function

    returns

    Column : the computed value of the function

37. def customMetricUDF(e: Column, metricName: String, metricFunction: (String, Column*) ⇒ Column, approxMethod: String, approxValue: Double): Column

    

    Customize function metric in the case continuous variabes used for : percentile 25, median and percentile75

    Customize function metric in the case continuous variabes used for : percentile 25, median and percentile75

    e

    : the column

    metricName

    : the name of the metric

    metricFunction

    : the metric function

    approxMethod

    : the approximation method

    approxValue

    : the value to pass to stat_method

38. def customStddev(e: Column): Column

    

    Customize Stddev of the column e

    Customize Stddev of the column e

    e

    : the name of the column

    returns

    Integer : the computed value of the Stddev

39. def customVariance(e: Column): Column

    

    Customize variance of the column e

    Customize variance of the column e

    e

    : the name of the column

    returns

    Integer : the computed value of the variance

40. def dataToMetricData(colNamDataCatCountFreq: (Column, DataFrame), operations: List[DiscreteMetric]): DataFrame

    

    Function to compute the Dataframe metric by variable

    Function to compute the Dataframe metric by variable

    colNamDataCatCountFreq

    : tuple of column variable and the Dataframe with Category, Count and Frequencies obtain from categoryCountFreqDataframe()

    operations

    : list of metrics you want to calculate.

    returns

    Dataframe : with all the values of discrete metrics

41. val discreteMetrics: List[DiscreteMetric]

    

    List of all available metrics.

42. final def eq(arg0: AnyRef): Boolean

    

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43. def equals(arg0: Any): Boolean

    

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44. def finalize(): Unit

    

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    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

45. final def getClass(): Class[_]

    

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46. def hashCode(): Int

    

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47. final def isInstanceOf[T0]: Boolean

    

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48. val logger: Logger

    

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    protected

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    StrictLogging

49. def metricCatCountFreq(dataCategoryCount: DataFrame): Column

    

    Function to extract the column that contains the list of struct cat_count_freq

50. def metricCategory(dataCategoryCount: DataFrame): Column

    

    Function to extract the column that contains the list of category

    Function to extract the column that contains the list of category

    dataCategoryCount

    : the data frame obtain from categoryCountFreqDataframe()

    returns

    Column : of that contain the list of category values

51. def metricCountDiscret(dataCategoryCount: DataFrame): Column

    

    Function to extract the column that contains the list of CountDiscret

    Function to extract the column that contains the list of CountDiscret

    dataCategoryCount

    : the data frame obtain from categoryCountFreqDataframe()

    returns

    Column : of that contain the list of CountDiscrete values

52. def metricCountDistinct(dataCategoryCount: DataFrame): Column

    

    Function to extract the column that contains the list of CountDistinct

    Function to extract the column that contains the list of CountDistinct

    dataCategoryCount

    : the data frame obtain from categoryCountFreqDataframe()

    returns

    Column : of that contain the list of CountDistinct values

53. def metricFrequency(dataCategoryCount: DataFrame): Column

    

    Function to extract the column that contains the list of frequencies

    Function to extract the column that contains the list of frequencies

    dataCategoryCount

    : the data frame obtain from categoryCountFreqDataframe()

    returns

    Column : of that contain the list of frequencies values

54. def metricMissingValues(dataCategoryCount: DataFrame): Column

    

    Function to extract the column that contains the list of number of Missing values

    Function to extract the column that contains the list of number of Missing values

    dataCategoryCount

    : the data frame obtain from categoryCountFreqDataframe()

    returns

    Column : of that contain the list of Missing Values values

55. final def ne(arg0: AnyRef): Boolean

    

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56. final def notify(): Unit

    

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57. final def notifyAll(): Unit

    

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58. def percentile25(e: Column): Column

    

    Customize percentile of order 0.25 of the column e

    Customize percentile of order 0.25 of the column e

    e

    : the column

    returns

    Integer : the computed value of the percentile of order 0.25

59. def percentile75(e: Column): Column

    

    Customize percentile of order 0.75 of the column e

    Customize percentile of order 0.75 of the column e

    e

    : the column

    returns

    Integer : the computed value of the percentile of order 0.75

60. def regroupContinuousMetricsByVariable(nameCol: String, metricFrame: DataFrame): DataFrame

    

    Function to regroup and reformat all metrics for a given variable

    Function to regroup and reformat all metrics for a given variable

    nameCol

    : the name of the column.

    metricFrame

    : the DataFrame of all the computed metrics for each variable by columns.

    returns

    : the DataFrame metric associated to the variable (namecol).

61. final def synchronized[T0](arg0: ⇒ T0): T0

    

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62. def toString(): String

    

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63. final def wait(): Unit

    

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64. final def wait(arg0: Long, arg1: Int): Unit

    

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65. final def wait(arg0: Long): Unit

    

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